Neural signatures of social inferences predict the number of real-life social contacts and autism severity

We regularly infer other people’s thoughts and feelings from observing their actions, but how this ability contributes to successful social behavior and interactions remains unknown. We show that neural activation patterns during social inferences obtained in the laboratory predict the number of social contacts in the real world, as measured by the social network index, in three neurotypical samples (total n = 126) and one sample of autistic adults (n = 23). We also show that brain patterns during social inference generalize across individuals in these groups. Cross-validated associations between brain activations and social inference localize selectively to the right posterior superior temporal sulcus and were specific for social, but not nonsocial, inference. Activation within this same brain region also predicts autism-like trait scores from questionnaires and autism symptom severity. Thus, neural activations produced while thinking about other people’s mental states predict variance in multiple indices of social functioning in the real world.


Table of Contents
Mean (± SD); Note that the why/how social inference task performed by the discovery sample (DS) differed in several details from the task version performed by the healthy replication sample (RS) and the ASD group. Source data are provided as a Source Data file.     Figure 1C;   [14]. B. Red illustrates the right pSTS ROI in our why/how task; green displays the cluster in the pSTS that encoded monetary benefits for other people in an altruistic choice task. Predictive neural information in this green cluster increased when people engaged in theory of mind ("think about how the other person will feel when confronted with your choice") in [15]. Yellow illustrates the overlap of both clusters. (DS).
Are there brain regions that encoded social inferences significantly more -or less -depending on the stimulus set (hands/faces) in the why/how task? To address this question, we compared whole-brain decoding accuracy maps of social inferences obtained for face blocks and hand blocks of the why/how task (see Table S4 and suggesting that images of intentional hand actions more effectively elicited social inferences in both areas (reflected in higher decoding accuracies). Importantly, the identified cluster in the pSTS/TPJ overlapped with the pSTS cluster described in Table 2. Overall, these supplemental results demonstrate a close match of recruited brain areas and predictive information across stimulus categories. The findings also suggest that our data-driven approach of defining ROIs did not systematically exclude brain areas selectively recruited for one but not the other stimulus set in the why/how task.
Anatomical data preprocessing A total of 6 T1-weighted (T1w) images were found within the input BIDS dataset. All of them were corrected for intensity non-uniformity (INU) with N4BiasFieldCorrection [3], distributed with ANTs 2.2.0 [4]. The T1w-reference was then skull-stripped with a Nipype implementation of the antsBrainExtraction.sh workflow (from ANTs), using OASIS30ANTs as target template. Brain tissue segmentation of cerebrospinal fluid (CSF), white-matter (WM) and gray-matter (GM) was performed on the brain-extracted T1w using fast [5] from FSL 5.0.9. A T1w-reference map was computed after registration of 6 T1w images (after intensity non-uniformity correction) using mri_robust_template from FreeSurfer 6.0.1 [6]. Volume-based spatial normalization to one standard space

Functional data preprocessing
For each of the BOLD runs found per subject (across all tasks and sessions), the following preprocessing was performed. First, a reference volume and its skull-stripped version were generated using a custom methodology of fMRIPrep. A B0-nonuniformity map (or fieldmap) was estimated based on two (or more) echo-planar imaging (EPI) references with opposing phase-encoding directions, with 3dQwarp from AFNI 20160207 [7]. Based on the estimated susceptibility distortion, a corrected EPI (echo-planar imaging) reference was calculated for a more accurate co-registration with the anatomical reference.
The BOLD reference was then co-registered to the T1w reference using flirt [8] from FSL 5.0.9, with the boundary-based registration cost-function [9]. Co-registration was configured with nine degrees of freedom to account for distortions remaining in the BOLD reference. Head-motion parameters with respect to the BOLD reference (transformation matrices, and six corresponding rotation and translation parameters) are estimated before any spatiotemporal filtering using mcflirt [8] from FSL 5.0.9. BOLD runs were slice-time corrected using 3dTshift from AFNI 20160207 [7]. The BOLD timeseries (including slice-timing correction when applied) were resampled onto their original, native space by applying a single, composite transform to correct for head-motion and susceptibility distortions.
These resampled BOLD time-series will be referred to as preprocessed BOLD in original space, or just preprocessed BOLD. The BOLD time-series were resampled into standard space, generating a preprocessed BOLD run in MNI152NLin6Asym space. First, a reference volume and its skull-stripped version were generated using a custom methodology of fMRIPrep. Automatic removal of motion artifacts using independent component analysis (ICA-AROMA) [10] was performed on the preprocessed BOLD on MNI space time-series after removal of non-steady state volumes and spatial smoothing with an isotropic, Gaussian kernel of 6mm FWHM (full-width half-maximum). Corresponding "nonaggresively" denoised runs were produced after such smoothing. Additionally, the "aggressive" noiseregressors were collected and placed in the corresponding confounds file. Several confounding timeseries were calculated based on the preprocessed BOLD: framewise displacement (FD), DVARS and three region-wise global signals. FD and DVARS are calculated for each functional run, both using their implementations in Nipype [following the definitions by [11]. The three global signals are extracted within the CSF, the WM, and the whole-brain masks. Additionally, a set of physiological regressors were extracted to allow for component-based noise correction (CompCor) [12]. Principal components are estimated after high-pass filtering the preprocessed BOLD time-series (using a discrete cosine filter with 128s cut-off) for the two CompCor variants: temporal (tCompCor) and anatomical (aCompCor).
tCompCor components are then calculated from the top 5% variable voxels within a mask covering the subcortical regions. This subcortical mask is obtained by heavily eroding the brain mask, which ensures it does not include cortical GM regions. For aCompCor, components are calculated within the intersection of the aforementioned mask and the union of CSF and WM masks calculated in T1w space, after their projection to the native space of each functional run (using the inverse BOLD-to-T1w transformation). Components are also calculated separately within the WM and CSF masks. For each CompCor decomposition, the k components with the largest singular values are retained, such that the retained components' time series are sufficient to explain 50 percent of variance across the nuisance mask (CSF, WM, combined, or temporal). The remaining components are dropped from consideration.
The head-motion estimates calculated in the correction step were also placed within the corresponding confounds file. The confound time series derived from head motion estimates and global signals were expanded with the inclusion of temporal derivatives and quadratic terms for each [13]. Frames that exceeded a threshold of 0.5 mm FD or 1.5 standardised DVARS were annotated as motion outliers. All resamplings can be performed with a single interpolation step by composing all the pertinent transformations (i.e. head-motion transform matrices, susceptibility distortion correction when available, and co-registrations to anatomical and output spaces). Gridded (volumetric) resamplings were performed using antsApplyTransforms (ANTs), configured with Lanczos interpolation to minimize the smoothing effects of other kernels. Non-gridded (surface) resamplings were performed using mri_vol2surf (FreeSurfer).